Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network.

Using information technology to extract emergency decision-making knowledge from emergency plan documents is an essential means to enhance the efficiency and capacity of emergency management. To address the problems of numerous terminologies and complex relationships faced by emergency knowledge extraction of water diversion project, a multi-feature graph convolutional network (PTM-MFGCN) based on pre-trained model is proposed. Initially, through the utilization of random masking of domain-specific terminologies during pre-training, the model's comprehension of the meaning and application of such terminologies within specific fields is enhanced, thereby augmenting the network's proficiency in extracting professional terminologies.

Drought prediction based on an improved VMD-OS-QR-ELM model.

To overcome the low accuracy, poor reliability, and delay in the current drought prediction models, we propose a new extreme learning machine (ELM) based on an improved variational mode decomposition (VMD). The model first redefines the output of the hidden layer of the ELM model with orthogonal triangular matrix decomposition (QR) to construct an orthogonal triangular ELM (QR-ELM), and then introduces an online sequence learning mechanism (OS) into the QR-ELM to construct an online sequence OR-ELM (OS-QR-ELM), which effectively improves the efficiency of the ELM model. The mutual information extension method was then used to extend both ends of the original signal to improve the VMD end effect.

[Influence of semen preservation and processing methods on sperm DNA integrity].

Objective: To investigate the influence of different methods of semen preservation and processing on sperm DNA integrity.

Methods: We collected semen samples from 100 normozoospermic male volunteers and, following homogeneous mixing, preserved them by means of snap freezing, slow freezing, or at the room temperature for 4 and 24 hours. Meanwhile we processed the semen by washing, swim-up, and density gradient centrifugation (DGC).

[Time interval from the end of sperm processing to artificial intrauterine in semination with husband's sperm correlates to the rate of clinical pregnancy].

Objective: To investigate the influence of the time interval from the end of semen processing to artificial intrauterine in semination with husband's sperm (AIH-IUI) on the rate of clinical pregnancy.

Methods: This study involved 191 AIH-IUI cycles with the same ovulation induction protocol. After Percoll density gradient centrifugation, we divided the sperm into four groups based on the incubation time: 0-19, 20-39, 40-59, and 60-80 min, and again into another four groups according to the total progressively motile sperm count (TPMC): (0-9), (10-20), (21-30), and > 30 x 10(6).